A financial company hires your company to develop back-propagation neural network(s) for predicting the next-week trend of two stocks (i.e. go up, go 3 down, or remain the same). In the meantime, the company also provides you the data for each stock in the past 5 years. Each data record consists of 20 attributes (such as index values, revenues, earnings per share, capital investment, and so on).
The team member A suggests that you should develop a single neural network that can handle both stocks.
But member B insists that you have to develop two separate networks (one for each stock). Whom do you think is correct, and why?